1887

Abstract

The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, S12 was grown in independent triplicate fermentations on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. RNA isolated from these samples was analysed in duplicate on an anonymous clone-based array to avoid bias during data analysis. The relevant transcripts were identified by analysing the loadings of the principal components (PC) and discriminants (D) in PCA and PCDA, respectively. Even more specifically, the relevant transcripts for a specific phenotype could also be ranked from the loadings under an angle (biplot) obtained after PCDA analysis. The leads identified in this way were compared with those identified using the commonly applied fold-difference and hierarchical clustering approaches. The different data analysis methods gave different results. The methods used were complementary and together resulted in a comprehensive picture of the processes important for the different carbon sources studied. For the more subtle, regulatory processes in a cell, the PCDA approach seemed to be the most effective. Except for glucose and gluconate dehydrogenase, all genes involved in the degradation of glucose, gluconate and fructose were identified. Moreover, the transcriptomics approach resulted in potential new insights into the physiology of the degradation of these carbon sources. Indications of iron limitation were observed with cells grown on glucose, gluconate or succinate but not with fructose-grown cells. Moreover, several cytochrome- or quinone-associated genes seemed to be specifically up- or downregulated, indicating that the composition of the electron-transport chain in S12 might change significantly in fructose-grown cells compared to glucose-, gluconate- or succinate-grown cells.

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2006-01-01
2020-09-24
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References

  1. Akopyants N. S, Clifton S. W, Martin J, Pape D, Wylie T, Li L, Kissinger J. C, Roos D. S, Beverley S. M. 2001; A survey of the Leishmania major Friedlin strain V1 genome by shotgun sequencing: a resource for DNA microarrays and expression profiling. Mol Biochem Parasitol113:337–340[CrossRef]
    [Google Scholar]
  2. Altschul S. F, Gish W, Miller W, Meyers E. W, Lipman D. J. 1990; Basic local alignment search tool. J Mol Biol215:403–410[CrossRef]
    [Google Scholar]
  3. Carpentier A.-S, Riva A, Tisseur P, Didier G, Henaut A. 2004; The operons, a criterion to compare the reliability of transcriptome analysis tools: ICA is more reliable than ANOVA, PLS and PCA. Comput Biol Chem28:3–10[CrossRef]
    [Google Scholar]
  4. Chapman S, Schenk P, Kazan K, Manners J. 2001; Using biplots to interpret gene expression patterns in plants. Bioinformatics18:202–204
    [Google Scholar]
  5. Eisen M. B, Spellman P. T, Brown P. O, Botstein D. 1998; Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A95:14863–14868[CrossRef]
    [Google Scholar]
  6. Enz S, Brand H, Orellana C, Mahren S, Braun V. 2003; Sites of interaction between the FecA and FecR signal transduction proteins of ferric citrate transport in Escherichia coli K-12. J Bacteriol185:3745–3752[CrossRef]
    [Google Scholar]
  7. Hager P. W, Calfee M. W, Phibbs P. V. 2000; The Pseudomonas aeruginosa devB/SOL homolog, pgl , is a member of the hex regulon and encodes 6-phosphogluconolactonase. J Bacteriol182:3934–3941[CrossRef]
    [Google Scholar]
  8. Hartmans S, van der Werf M. J, de Bont J. A. M. 1990; Bacterial degradation of styrene involving a novel flavin adenine dinucleotide-dependent styrene monooxygenase. Appl Environ Microbiol56:1347–1351
    [Google Scholar]
  9. Hassett D. J, Howell M. L, Ochsner U. A, Vasil M. L, Johnson Z, Dean G. E. 1997; An operon containing fumC and sodA encoding fumarase C and manganese superoxide dismutase is controlled by the ferric uptake regulator in Pseudomonas aeruginosa : fur mutants produce elevated alginate levels. J Bacteriol179:1452–1459
    [Google Scholar]
  10. Heyer L. J, Kruglyak S, Yooseph S. 1999; Exploring expression data: indentification and analysis of coexpressed genes. Genome Res9:1106–1115[CrossRef]
    [Google Scholar]
  11. Hoogerbrugge R, Willig S. J, Kistemaker P. G. 1983; Discriminant analysis by double stage principal component analysis. Anal Chem55:1710–1712[CrossRef]
    [Google Scholar]
  12. Lessie T. G, Phibbs P. V. 1984; Alternative pathways of carbohydrate utilization in Pseudomonas . Annu Rev Microbiol38:359–387[CrossRef]
    [Google Scholar]
  13. Matsushita K, Ameyama M. 1982; d-Glucose dehydrogenase from Pseudomonas fluorescens , membrane-bound. Methods Enzymol89:149–154
    [Google Scholar]
  14. Matsushita K, Shinagawa E, Adachi O, Ameyama M. 1979; Membrane-bound d-gluconate dehydrogenase from Pseudomonas aeruginosa . J Biochem85:1173–1181
    [Google Scholar]
  15. Michaud D. J, Marsh A. G, Dhurjati P. S. 2003; eXPatGen: generating dynamic expression patterns for the systematic evaluation of analytical methods. Bioinformatics19:1140–1146[CrossRef]
    [Google Scholar]
  16. Moeck G. S, Coulton J. W. 1998; TonB-dependent iron acquisition: mechanisms of siderophore-mediated active transport. Mol Microbiol28:675–681
    [Google Scholar]
  17. Nelson K. E, Weinel C, Paulsen I. T.40 other authors 2002; Complete genome sequence and comparative analysis of the metabolically versatile Pseudomonas putida KT2440. Environ Microbiol4:799–808[CrossRef]
    [Google Scholar]
  18. Orr M. S, Scherf U. 2002; Large-scale gene expression analysis in molecular target discovery. Leukemia16:473–477[CrossRef]
    [Google Scholar]
  19. Petruschka L, Adlf K, Burchardt G, Dernedde J, Jurgensen J, Hermann H. 2002; Analysis of the zwf-pgl-eda operon in Pseudomonas putida strains H and KT2440. FEMS Microbiol Lett215:89–95[CrossRef]
    [Google Scholar]
  20. Pieterse B, Jellema R. H, van der Werf M. J. 2005; Quenching of microbial samples for increased reliability of microarray data. J Microbiol Methods [CrossRef]
    [Google Scholar]
  21. Quackenbush J. 2001; Computational analysis of microarray data. Nat Rev Genet2:418–427[CrossRef]
    [Google Scholar]
  22. Redly G. A, Poole K. 2003; Pyoverdine-mediated regulation of FpvA synthesis in Pseudomonas aeruginosa : involvement of a probable extracytoplasmic-function sigma factor, FpvI. J Bacteriol185:1261–1265[CrossRef]
    [Google Scholar]
  23. Sage A. E, Proctor W. D, Phibbs P. V. 1996; A two-component response regulator, gltR , is required for glucose transport activity in Pseudomonas aeruginosa PAO1. J Bacteriol178:6064–6066
    [Google Scholar]
  24. Slonim D. K. 2002; From patterns to pathways: gene expression data analysis comes of age. Nat Genet Suppl32:S502–S508[CrossRef]
    [Google Scholar]
  25. Stinson M. W, Cohen M. A, Merrick J. M. 1977; Purification and properties of the periplasmic glucose-binding protein of Pseudomonas aeruginosa . J Bacteriol131:672–681
    [Google Scholar]
  26. Swanson B. L, Hager P, Phibbs P, Ochsner U, Vasil M, Hamood A. N. 2000; Characterization of the 2-ketogluconate utilization operon in Pseudomonas aeruginosa PAO1. Mol Microbiol37:561–573
    [Google Scholar]
  27. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander E. S, Golub T. R. 1999; Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A96:2907–2912[CrossRef]
    [Google Scholar]
  28. Tavazoie S, Hughes J. D, Campbell M. J, Cho R. J, Church G. M. 1999; Systematic determination of genetic network architecture. Nat Genet22:281–285[CrossRef]
    [Google Scholar]
  29. Tefferi A, Bolander M. E, Ansell S. M, Wieben E. D, Spelsberg T. C. 2002; Primer on medical genomics. Part III: microarray experiments and data analysis. Mayo Clin Proc77:927–940[CrossRef]
    [Google Scholar]
  30. Temple L. M, Sage A. E, Schweizer H. P, Phibbs P. V. 1998; Carbohydrate catabolism in Pseudomonas aeruginosa. In Pseudomonas pp 35–72 Edited by Montie T. C.. New York: Plenum;
    [Google Scholar]
  31. van der Werf M. J. 2005; Towards replacing closed with open target selection strategies. Trends Biotechnol23:11–16[CrossRef]
    [Google Scholar]
  32. van der Werf M. J, Jellema R. H, Hankemeier T. 2005; Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets. J Ind Microbiol Biotechnol32:234–252[CrossRef]
    [Google Scholar]
  33. Wan C.-Y, Wilkins T. A. 1994; A modified hot borate method significantly enhances the yield of high-quality RNA from cotton (Gossypium hirsutum L.). Anal Biochem223:7–12[CrossRef]
    [Google Scholar]
  34. Wu T. D. 2001; Analysing gene expression data from DNA microarrays to identify candidate genes. J Pathol195:53–65[CrossRef]
    [Google Scholar]
  35. Wylie J. L, Worobec E. A. 1994; Cloning and nucleotide sequence of the Pseudomonas aeruginosa glucose-selective OrpB porin gene and distribution of oprB within the family Pseudomonaceae . Eur J Biochem220:505–512[CrossRef]
    [Google Scholar]
  36. Wylie J. L, Worobec E. A. 1995; The OprB porin plays a central role in carbohydrate uptake in Pseudomonas aeruginosa . J Bacteriol177:3021–3026
    [Google Scholar]
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